
import pandas as pd
import numpy as np
import plotly.express as px  # Be sure to import express
from IPython.display import display
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud,ImageColorGenerator


originData = pd.read_csv(
    "data/itcont_2020_20200722_20200820.txt", sep='|')
stateAmt =  originData[['STATE', 'TRANSACTION_AMT']]
doantionPerState = stateAmt.groupby('STATE')['TRANSACTION_AMT'].sum()

c:\users\delta\appdata\local\programs\python\python39\lib\site-packages\IPython\core\interactiveshell.py:3441: DtypeWarning: Columns (10,15,16,18) have mixed types.Specify dtype option on import or set low_memory=False.
  exec(code_obj, self.user_global_ns, self.user_ns)


d = pd.DataFrame({'state' : doantionPerState.index,'value' : doantionPerState})
fullName = pd.read_json('./state.json',orient='index')
fullName = pd.DataFrame({'state' : fullName.index,'full':fullName[0]})
d = d.merge(fullName,how='left')


fig = px.choropleth(d,  # Input Pandas DataFrame
                    locations="state",  # DataFrame column with locations
                    color="value",  # DataFrame column with color values
                    hover_name="full", # DataFrame column hover info
                    locationmode = 'USA-states') # Set to plot as US States
fig.update_layout(
    title_text = '美国各州捐款总额', # Create a Title
    geo_scope='usa',  # Plot only the USA instead of globe
)
fig.show()  # Output the plot to the screen


data = originData[['CMTE_ID','TRANSACTION_AMT','TRANSACTION_DT','NAME']] # MMDDYYYY
cclData = pd.read_csv("data/ccl.txt",sep='|')[['CAND_ID','CMTE_ID']]
td = data.merge(cclData)
dname = pd.read_csv("data/weball20.txt",sep='|')[['CAND_ID','CAND_NAME']]
td = td.merge(dname)
td['TRANSACTION_DT'] = pd.to_datetime(td['TRANSACTION_DT'],format='%m%d%Y')
std = td.groupby('CAND_NAME')['TRANSACTION_AMT'].sum().sort_values(ascending=False)


sns.set()
%matplotlib inline

for member in (std.index[:3]):
   # print(member)
    tmp = td[td['CAND_NAME'] == member]
    moneyPerDay = tmp.groupby('TRANSACTION_DT')['TRANSACTION_AMT'].sum()
    money = np.cumsum(moneyPerDay)
    money.index.name = 'Date'

    # display(money.index)
    plt.plot(money,label=member)
    
plt.tight_layout()
plt.legend()
# fig = plt.figure(figsize=(40,4))
plt.tick_params(axis='x', labelsize=8) 
plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False #用来正常显示负号
plt.xlabel(u'日期')
plt.ylabel(u"获得捐赠数额（美元）")

Text(34.25, 0.5, '获得捐赠数额（美元）')


biden = td[td['CAND_NAME'] == 'BIDEN, JOSEPH R JR']['NAME']

data = ','.join(biden.to_list())
# 读取图片文件
bg = plt.imread("data/biden.jpg")
# 生成
wc = WordCloud(  # FFFAE3
    background_color="black",  # 设置背景为白色，默认为黑色
    width=1920,  # 设置图片的宽度
    height=1080,  # 设置图片的高度
    mask=bg,    # 画布
    margin=10,  # 设置图片的边缘
    max_font_size=100,  # 显示的最大的字体大小
    random_state=20,  # 为每个单词返回一个PIL颜色
).generate_from_text(data)
# 图片背景
bg_color = ImageColorGenerator(bg)
# 开始画图
plt.imshow(wc.recolor(color_func=bg_color))
# 为云图去掉坐标轴
plt.axis("off")
# 画云图，显示
# 保存云图
wc.to_file("biden.png")

<wordcloud.wordcloud.WordCloud at 0x24250ccc6a0>

实验题目2：美国总统大选数据分析¶

1、统计各个州的捐款总额，并在美国地图上画出各州捐款总额的热度图（heatmap），颜色越深的州代表捐款额越多，要求图表美观易懂。¶

2、统计获得捐赠额最多的三位候选人的捐赠额变化趋势，使用折线图展示，横轴表示时间，纵轴表示捐赠额，要求图表美观易懂。¶

3、分析出获得捐款额最多的候选人，然后将此候选人的捐赠者的姓名画成词云图。¶